Neuroimaging and kinematic biomarkers of post-stroke upper limb motor impairment
Bibliographic record
Abstract
Structural and functional biomarkers derived from magnetic resonance imaging explain some variance in post-stroke motor impairment. The understanding of the nature of impairment and the discrimination between true behavioural motor recovery/restitution and motor compensation may be improved by the addition of kinematic information. The aim of the study was to determine the influence of neuroimaging combined with kinematic biomarkers in explaining the variance in motor impairment of the upper limb. People living with late sub-acute to chronic stroke (n = 25) underwent the Fugl Meyer Assessment – Upper Limb (FMA-UL), magnetic resonance imaging, and completed a reaching task where upper limb and trunk kinematics were recorded. Regression analyses were performed to determine the amount of variability in FMA-UL explained by the following biomarkers: the amount of corticospinal tract impacted by the stroke lesion (CST involvement), interhemispheric and ipsilesional resting state connectivity, and the Trunk-based Index of Performance (IPt) that measures skilled reaching ability while accounting for trunk compensation. CST involvement, interhemispheric connectivity, and the IPt, together explained ∼ 49 % of the variance in the FMA-UL (F(3,21) = 8.694, p = 0.001, R 2 adj = 0.49). The IPt explained an additional 14 % of the variance in the FMA-UL compared to CST involvement alone (p = 0.02). The IPt is a relevant kinematic biomarker of post-stroke upper limb motor impairment. Our findings suggest the importance of using multiple categories of biomarkers to better understand the level of post-stroke motor impairment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".